System identification based on online variational bayes method and its application to reinforcement learning

Junichiro Yoshimoto, Shin Ishii, Masa Aki Sato

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

In this article, we present an on-line variational Bayes (VB) method for the identification of linear state space models. The learning algorithm is implemented as alternate maximization of an on-line free energy, which can be used for determining the dimension of the internal state. We also propose a reinforcement learning (RL) method using this system identification method. Our RL method is applied to a simple automatic control problem. The result shows that our method is able to determine correctly the dimension of the internal state and to acquire a good control, even in a partially observable environment.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsOkyay Kaynak, Ethem Alpaydin, Erkki Oja, Lei Xu
PublisherSpringer Verlag
Pages123-131
Number of pages9
ISBN (Print)3540404082, 9783540404088
DOIs
Publication statusPublished - 2003
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2714
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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